
VGGFace [1]} is a 16-layer CNN, trained on the VGGFace dataset comprising over 2M celebrity images. Given a \(224\!\times \!224\)  input image, the network extracts 4,096 image features from the output of the 6-th fully connected layer.

VGG16 [2]} constitutes the same 16-layer CNN, however trained on over 1M images from the ImageNet database [3]}.
We use 4,096 features, provided as output by the second fully connected layer.

ResNet50 [4]} represents a 50-layer CNN, also trained on ImageNet. As a result of having a large amount of layers, the network has learned rich feature representations for a wide range of images. Similar as the above networks, ResNet50 accepts as input an image of size \(224\!\times \!224\) . The 2,048 related ResNet50 features are provided by the last convolutional layer.

InceptionV3 [5]} is a 48-layer CNN trained on ImageNet. We obtain 2,048 features from the last fully connected layer.

MobileNet [6]} is a 56-layer CNN trained on ImageNet, which has been optimized for mobile devices. MobileNet extracts a 1,000-dimensional feature vector.

DenseNet121 [7]} is a 121-layer CNN trained on ImageNet. The layer structure involves more narrow layers as opposed to ResNet50. The total number of layers is determined by 5 plus two blocks of 58 layers, the last of which results in a 1,024-dimensional feature vector.

